The PV Corrosion Fault Prognosis Based on Ensemble Kalman Filter

International audienceThe degradation of photovoltaic (PV) modules remains a major concern on the control and the development of the photovoltaic field, particularly, in regions with difficult climatic conditions. The main degradation modes of the PV modules are corrosion, discoloration, glass breaks, and cracks of cells. However, corrosion and discoloration remain the predominant degradation modes that still require further investigations. In this paper, a model-based PV corrosion prognostic approach, based on an ensemble Kalman filter (EnKF), is introduced to identify the PV corrosion parameters and then estimate the remaining useful life (RUL). Simulations have been conducted using measured data set, and results are reported to show the efficiency of the proposed approach

Predicting the Future is like Completing a Painting!

This article is an introductory work towards a larger research framework relative to Scientific Prediction. It is a mixed between science and philosophy of science, therefore we can talk about Experimental Philosophy of Science. As a first result, we introduce a new forecasting method based on image completion, named Forecasting Method by Image Inpainting (FM2I). In fact, time series forecasting is transformed into fully images- and signal-based processing procedures. After transforming a time series data into its corresponding image, the problem of data forecasting becomes essentially a problem of image inpainting problem, i.e., completing missing data in the image. An extensive experimental evaluation is conducted using a large dataset proposed by the well-known M3-competition. Results show that FM2I represents an efficient and robust tool for time series forecasting. It has achieved prominent results in terms of accuracy and outperforms the best M3 forecasting methods.Comment: 25 pages, 12 figure

University-Based Smart Cities: from collective intelligence to smart crowd-conscience

Quality of life, economic, knowledge and human capitals ‘development are the main challenges of the new wave of smart cities. Hybrid strategies of cost leadership and innovation need to be aligned mostly by highly deliberate university creative services.  Physical, intellectual and social capitals are loosely coupled to better understanding of the urban fabric and norms of behavior. It requires the creation ofapplications enabling data collection and processing, web-based collaboration, and “real-time†mining of the collective intelligence of citizens. The Internet of Things (IoT) has been viewed as a promising technology with great potential for addressing many societal challenges, filling the gap in terms of citizen's sensitivity measurement. At the physical level of its ecosystem, buildings are responsible for about 40% of energy consumption in cities and more than 40% of greenhouse gas emissions. With recent products available today, energy consumption in buildings could be cut by up to 70 percent, but it requires an integrated and collective adaptive framework to show how buildings are operated, maintained and controlled with the support of IoT-based innovation and solutions. The number of new IoT protocols and applications has grown exponentially in recent years. However, IoT for smart cities needs accessible open data and open systems, so that industries and universities can develop new services and applications. The main aim is to develop energy efficient frameworks to improve energy efficiency by using innovative integrated IoT techniques. These techniques could integrate technologies from context-aware computing, context-dependent user expectation and profile and occupants' actions and behaviors. This paper tend to present in what extent a case of university-based smart city would invest in IoT as both strategy and process in order to enhance efficiency, innovative education and attractiveness for its current and future citizens

Design and Evaluation of a Distributed SDN Control Plane Architecture

Software-defined networking (SDN) is a new networking paradigm which aims to decouple forwarding hardware from control decisions. Existing SDN architectures centralize control logic to networks, and, however, cannot meet the requirements of scalability and adaptability simultaneously. Issues are that SDN architectures must be able to scale and able to adapt to dynamic conditions. Therefore, in order to cope with large-scale and dynamically changing networks, new architectures are required and need to be highly flexible. In this paper, an architecture that organizes controllers in a distributed hierarchy is introduced. Design concepts are presented to highlight its operation modes. Simulations have been conducted using Mininet and preliminary results are reported to show that the proposed architecture could reduce setup time of flow initiation

Moroccan Two-phase olive mill wastes hydrothermal carbonization: effect of water phase recycling on hydrochar yields and properties

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Approche distribuée et auto-adaptative pour la régulation de la taille d'une population d’agents mobiles dans un réseau

National audienceDans cet article, une approche distribuée et adaptative inspirée du système immunitaire pour la régulation d'une population d'agents mobiles pouvant se cloner dans un réseau est présentée. Il s'agit d'une approche fondée sur l'utilisation d'un modèle de comportement d'agents, dans lequel un mécanisme de décision local leur permet de sélectionner le comportement le plus approprié en fonction de l'état de leur environnement sans recourir à des paramètres définis d'une manière globale ou fixés à priori. La régulation de la taille de la population émerge, d'une manière globale, comme résultante des actions locales des agents autonomes distribués dans le réseau

A predictive control approach for thermal energy management in buildings

Building equipment accounts for almost 40% of total global energy consumption. More than half of which is used by active systems, such as heating, ventilation and air conditioning (HVAC) systems. These latter are responsible for the occupants’ well-being and considered among the main consumers of electricity in buildings. In order to improve both occupants’ comfort and energy efficiency in buildings, optimal control oriented models, such as Model Predictive Control (MPC), have proven to be promising techniques for developing intelligent control strategies for building energy management systems. This paper presents a real-time predictive control approach of an air conditioning (AC) system for thermal regulation in a single-zone building using MPC control framework. The proposed approach takes into account the physical parameters of the building, weather predictions (i.e. ambient temperature and solar radiation) and time-varying thermal comfort constraints to maintain optimal energy consumption of the AC while enhancing occupants’ comfort. For this purpose, a control-oriented thermal model for a room integrated with AC system is first developed using physics-based (white box) technique and then used to design and develop the MPC controller model. A numerical case study has been investigated and simulation results show the effectiveness of the proposed approach in reducing the energy consumption by about 68% while providing a significant indoor thermal improvement. A conventional On–Off controller was used as a baseline reference to evaluate the system performance against the proposed approach

Towards an Efficient Implementation of Human Activity Recognition for Mobile Devices

The availability of diverse and powerful sensors embedded in modern Smartphones/mobile devices has created exciting opportunities for developing context-aware applications. Although there is good capacity for collecting and classifying human activity data with such devices, data pre-processing and model building techniques that achieve this goal are required to operate while meeting hardware resource constraints, particularly for real-time applications. In this paper, we present a comparison study for HAR exploiting feature selection approaches to reduce the computation and training time needed for the discrimination of targeted activities while maintaining significant accuracy. We validated our approach on a publicly available dataset. Results show that Recursive Feature Elimination method combined with Radial Basis Function Support Vector Machine classifier offered the best tradeoff between training time/recognition performance

Energy evaluation of AID protocol in Mobile Ad Hoc Networks

International audienceMobile Ad Hoc Networks (MANETs) are communication networks formed on the fly by radio-equipped mobile nodes without relying on any fixed infrastructure. Flooding is the simplest technique for information dissemination in ad hoc based networks, in which nodes disseminate a received message to all their neighbors. This algorithm leads to the broadcast storm problem that severely affects the energy consumption due to redundant submissions. To regulate redundant submissions, which can cause more collisions and requires more energy, recently there have been developed numerous broadcasting techniques. These techniques have been mainly proposed to solve the storm problem by preventing certain nodes from rebroadcasting received messages and/or by differentiating the timing of rebroadcasts. In this paper, we have evaluated and compared an adaptive information dissemination (AID) algorithm with other MANETs broadcasting protocols with respect to the energy efficiency. In AID, each node can dynamically adjust the values of its local parameters using information from neighboring nodes without requiring any additional effort, such as distance measurements or exact location-determination of nodes. Simulation results are reported and show that adaptive broadcasting schemes are most efficient with respect to save broadcast, energy consumption, and reachability

Mutualistic relationships in service-oriented communities and fractal social organizations

In this paper we consider two social organizations -- service-oriented communities and fractal organizations -- and discuss how their main characteristics provide an answer to several shortcomings of traditional organizations. In particular, we highlight their ability to tap into the vast basins of "social energy" of our societies. This is done through the establishing of mutualistic relationships among the organizational components. The paper also introduces a mathematical model of said mutualistic processes as well as its translation in terms of semantic service description and matching. Preliminary investigations of the resilience of fractal social organizations are reported. Simulations show that fractal organizations outperform non-fractal organizations and are able to quickly recover from disruptions and changes characterizing dynamic environments.Comment: Pre-camera-ready paper to appear in the Proceedings of WCCS 2014 (2nd World Conference on Complex Systems